Social-ecological connections across land, water, and sea demand a reprioritization of environmental management

Despite many sectors of society striving for sustainability in environmental management, humans often fail to identify and act on the connections and processes responsible for social-ecological tipping points. Part of the problem is the fracturing of environmental management and social-ecological research into ecosystem domains (land, freshwater, and sea), each with different scales and resolution of data acquisition and distinct management approaches. We present a perspective on the social-ecological connections across ecosystem domains that emphasize the need for management reprioritization to effectively connect these domains. We identify critical nexus points related to the drivers of tipping points, scales of governance, and the spatial and temporal dimensions of social-ecological processes. We combine real-world examples and a simple dynamic model to illustrate the implications of slow management responses to environmental impacts that traverse ecosystem domains. We end with guidance on management and research opportunities that arise from this cross-domain lens to foster greater opportunity to achieve environmental and sustainability goals.Peer reviewe

Elevated nonlinearity as an indicator of shifts in the dynamics of populations under stress

International audienc

Ranking species based on sensitivity to perturbations under non‐equilibrium community dynamics

International audienceEcological communities are subject to external perturbations such as fires, storms, pollution, and overfishing, which are increasing in magnitude and frequency due to anthropogenic impacts (Barlow et al., 2018; Jackson et al., 2001; Turner et al., 1997). Indeed, strong and frequent perturbations can lead to species extinctions and, as a consequence, to the loss of critical ecosystem services (Cardinale et al., 2012; Levin & Lubchenco, 2008)

ESM1 from Elevated nonlinearity as an indicator of shifts in the dynamics of populations under stress

Populations occasionally experience abrupt changes, such as local extinctions, strong declines in abundance or transitions from stable dynamics to strongly irregular fluctuations. Although most of these changes have important ecological and at times economic implications, they remain notoriously difficult to detect in advance. Here, we study changes in the stability of populations under stress across a variety of transitions. Using a Ricker-type model, we simulate shifts from stable point equilibrium dynamics to cyclic and irregular boom–bust oscillations as well as abrupt shifts between alternative attractors. Our aim is to infer the loss of population stability before such shifts based on changes in nonlinearity of population dynamics. We measure nonlinearity by comparing forecast performance between linear and nonlinear models fitted on reconstructed attractors directly from observed time series. We compare nonlinearity to other suggested leading indicators of instability (variance and autocorrelation). We find that nonlinearity and variance increase in a similar way prior to the shifts. By contrast, autocorrelation is strongly affected by oscillations. Finally, we test these theoretical patterns in datasets of fisheries populations. Our results suggest that elevated nonlinearity could be used as additional indicator to infer changes in the dynamics of populations under stress

Quinidine: an “Endangered Species” Drug Appropriate for Management of Electrical Storm in Brugada Syndrome

AbstractBrugada syndrome is an inherited channelopathy associated with an increased risk of syncope and sudden cardiac death. In rare cases it can be manifested with electrical storm. We report two cases of Brugada syndrome that presented with electrical storm and were treated successfully with oral quinidine, an "endangered species" drug

Novel non-invasive P wave analysis for the prediction of paroxysmal atrial fibrillation recurrences in patients without structural heart disease: a prospective pilot study

OBJECTIVES: The pathogenetic mechanisms responsible for the initiation and recurrence of PAF are not fully elucidated and vary among individuals. We evaluated the ability of a novel non-invasive approach based on P wave wavelet analysis to predict symptomatic paroxysmal atrial fibrillation (PAF) recurrences in individuals without structural heart disease. METHODS: We studied 50 patients (24 males, mean age 54.9 ± 9.8 years) presented to our emergency department with a symptomatic episode of PAF. The patients were followed-up for 12.1 ± 0.1 months and classified into two groups according to the number of PAF episodes: Group A (5 PAF episodes vs. 0.1), whereas Group A and B patients had longer P waves at Z lead compared to Group C (86.4 ± 13 vs. 71.5 ± 15 msec, p < 0.001). CONCLUSIONS: P wave wavelet analysis can reliably predict the generation and recurrence of PAF within a year. P wave wavelet analysis could contribute to the early identification of patients at risk for increased number of PAF recurrences

P wave analysis with wavelets identifies hypertensive patients at risk of recurrence of atrial fibrillation: A case-control study and 1year follow-up

AIMS: Hypertension is a major risk factor for atrial fibrillation (AF); however, reliable non-invasive tools to assess AF risk in hypertensive patients are lacking. We sought to evaluate the efficacy of P wave wavelet analysis in predicting AF risk recurrence in a hypertensive cohort. METHODS: We studied 37 hypertensive patients who presented with an AF episode for the first time and 37 age- and sex-matched hypertensive controls without AF. P wave duration and energy variables were measured for each subject [i.e. mean and max P wave energy along horizontal (x), coronal (y) and sagittal (z) axes in low, intermediate and high frequency bands]. AF-free survival was assessed over a follow-up of 12.1±0.4months. RESULTS: P wave duration (Pdurz) and mean P wave energy in the intermediate frequency band across sagittal axis (mean2z) were independently associated with baseline AF status (p=0.008 and p=0.001, respectively). Based on optimal cut-off points, four groups were formed: Pdurz<83.2ms/mean2z<6.2μV(2) (n=23), Pdurz<83.2ms/mean2z≥6.2μV(2) (n=10), Pdurz≥83.2ms/mean2z<6.2μV(2) (n=22) and Pdurz≥83.2ms/mean2z≥6.2μV(2) (n=19). AF-free survival decreased (Log Rank p<0.0001) from low risk (Pdurz<83.2ms/mean2z<6.2μV(2)) to high-risk group (Pdurz≥83.2ms/mean2z≥6.2μV(2)). Patients presenting with longer and higher energy P waves were at 18 times higher AF risk compared to those with neither (OR: 17.6, 95% CI: 3.7-84.3) even after adjustment for age, sex, hypertension duration, left atrial size, beta-blocker, ACEi/ARBs and statin therapy. CONCLUSIONS: P wave temporal and energy characteristics extracted using wavelet analysis can potentially serve as screening tool to identify hypertensive patients at risk of AF recurrence